forked from moreo/QuaPy
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
import quapy as qp
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import numpy as np
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from os import makedirs
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import sys, os
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import pickle
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import argparse
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from common import *
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from uci_experiments import result_path
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from tabular import Table
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from uci_experiments import *
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import itertools
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tables_path = './tables_uci'
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MAXTONE = 50 # sets the intensity of the maximum color reached by the worst (red) and best (green) results
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makedirs(tables_path, exist_ok=True)
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qp.environ['SAMPLE_SIZE'] = SAMPLE_SIZE
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METHODS = [#'cc', 'acc',
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# 'pcc',
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# 'pacc',
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# 'wpacc',
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'pcc.opt',
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'pacc.opt',
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'wpacc.opt',
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'ds.opt',
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# 'pcc.opt.svm',
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# 'pacc.opt.svm',
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# 'wpacc.opt.svm',
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# 'wpacc.opt2',
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# 'MAX', 'MS', 'MS2',
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'sldc',
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# 'svmmae',
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# 'hdy',
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# 'ehdymaeds',
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# 'EMdiag', 'EMfull', 'EMtied', 'EMspherical',
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# 'VEMdiag', 'VEMfull', 'VEMtied', 'VEMspherical',
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]
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if __name__ == '__main__':
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results = 'results_uci'
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datasets = qp.datasets.UCI_DATASETS
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datasets.remove('acute.a')
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datasets.remove('acute.b')
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datasets.remove('iris.1')
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evaluation_measures = [qp.error.ae, qp.error.rae, qp.error.kld]
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for i, eval_func in enumerate(evaluation_measures):
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eval_name = eval_func.__name__
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# Tables evaluation scores for the evaluation measure
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# ----------------------------------------------------
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# fill data table
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table = Table(benchmarks=datasets, methods=METHODS)
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for dataset, method, run in itertools.product(datasets, METHODS, range(N_FOLDS*N_REPEATS)):
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table.add(dataset, method, experiment_errors(results, dataset, method, run, eval_name, optim_loss='ae'))
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# write the latex table
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nmethods = len(METHODS)
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tabular = """
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\\resizebox{\\textwidth}{!}{%
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\\begin{tabular}{|c||""" + ('c|' * nmethods) + '|' + """} \hline
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& \multicolumn{""" + str(nmethods) + """}{c||}{Quantification methods} \\\\ \hline
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"""
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rowreplace={dataset: nicename(dataset) for dataset in datasets}
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colreplace={method: nicename(method, eval_name, side=True) for method in METHODS}
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tabular += table.latexTabular(benchmark_replace=rowreplace, method_replace=colreplace)
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tabular += 'Rank Average & ' + table.getRankTable().latexAverage()
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tabular += """
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\end{tabular}%
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}
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"""
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save_table(f'{tables_path}/tab_results_{eval_name}.tex', tabular)
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print("[Done]") |